Neural paths for workload execution

ABSTRACT

In one example, a neural network model may be generated for a computing network having a plurality of computing resources. Performance metrics and historical usage patterns are the basis for learning current utilization patterns of the computing resources. A workload capacity for each one of the computing resources may be determined, and a neural path may be generated for execution of a specified workload.

BACKGROUND

Many organizations have multiple computing resources. For example, anorganization may have a computing system for each member of theorganization. Such computing systems can facilitate the execution ofmultiple workloads by member computing systems. Each member organizationcan utilize its own computing resource for workload execution. Workloadcapacity of the network's computing resources may be in MIPS (millionsof instructions per second). MIPS may generally indicate the number ofmachine instructions that a computing resource can execute in one secondalthough different instructions require more or less time than others.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of the disclosure will be rendered by reference to specificexamples thereof which are illustrated in the appended drawings. Thedrawings illustrate only particular examples of the disclosure andtherefore are not to be considered to be limiting of its scope. Theprinciples herein are described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1A illustrates example instructions stored on an examplenon-transitory computer-readable storage medium to implement artificialintelligence-based distributed workload execution according to thepresent disclosure.

FIG. 1B illustrates an example computing device according to the presentdisclosure.

FIG. 2 illustrates an example artificial intelligence-based workloadexecution process according to some examples of the present disclosure.

FIG. 3 illustrates an example network of computing systems to implementartificial intelligence-based distributed workload execution accordingto some examples of the present disclosure.

FIG. 4 illustrates a nodal network of computing resources according toan example of the present disclosure.

DETAILED DESCRIPTION

As noted above, organizations may have multiple computing resources tofacilitate execution of workloads by each member. However, although manysuch computing resources are utilized during business hours, they remainidle or in sleep mode during off-peak or non-business hours. Thisnon-usage can be said to represent a 66% reduction in availablecomputing capacity. Even worse, the computing resources continue toconsume power during periods of non-use. Computing resources may also beunderutilized because it is difficult to ascertain whether a host userof the computing resource is using the computing resource at fullcapacity. Thus, in some instances, in order to execute a desiredworkload, an organization may incur the cost of additional computingresources or data transformation while having idle computing resources.In addition, during big data ingestion, or the ETL (extract, transform,load) process, a large number of virtual machines may run to copy datafrom one computing resource to another. During this copy process, thedata structure may be changed to meet data store demand.

Accordingly, examples of the present disclosure utilize a neural networkmodel to facilitate execution of workloads by a network of computingsystems. In some examples of the present disclosure, a network ofcomputing systems may be used to execute a workload. A neural networkmodel may include a number of computing systems interconnected so thatdata may be propagated to every computing system of the network. Thenetwork may learn by repeatedly evaluating data, generating predictionsbased upon the evaluations, and adjusting outcomes based upon theaccuracy of the predictions.

In one example, the network may learn through training by comparingpredictions to known outcomes. As training progresses, the predictionsof the neural network model may become increasingly accurate. Forexample, the computing systems of a network may have a neural networkmodel that learns current utilization patterns of each computing systemto determine a workload capacity. The neural network model may thengenerate a neural path for execution of a specified workload. A neuralpath is a path through the network that may provide the capacity to meetthe execution cost for a workload. The path is considered a neural pathbecause it is continually revised based upon the neural network learningthrough a process of trial and error.

For some examples, the neural path may be dynamically updated uponchanges to the workload capacity of any of the computing systems. Forsome examples, the estimated execution cost for a specified workload maybe determined by simulated workload executions or by reference to pastworkload executions.

In one example, computing systems of a network may have a neural networkmodel that learns current utilization patterns of each computing systemto determine a workload capacity. The neural network model may thengenerate a neural path for execution of a specified workload. In thismanner, the path to and the work capacity of an underutilized computingresource is identified for a specified work capacity. Organizations neednot procure additional computing resources as they can utilize existingcomputing resources that would otherwise remain idle.

In one example of the present disclosure, the neural path may bedynamically updated based upon changes to the workload capacity of anyof the computing systems. In one example, the estimated capacity for aspecified workload may be determined by simulated workload executions orby reference to past workload executions.

In one example, the neural network model calculates system utilizationof each computing system by measuring performance related metrics thatmay include CPU utilization, RAM utilization, disk I/O activity andnetwork activity. As the workload is executed, additional utilizationinformation for each computing system may be obtained and used to revisea neural path for execution of a workload and to more accurately predictworkload capacity.

The neural network model may, over time and after multiple executions,learn the patterns of computing resource availability to increaseaccuracy of predicted workload capacity. As more data is analyzed, theneural network model may become more accurate and efficient. Accuratepredictions may allow workloads to be executed across multiple computingsystems with less impact on the primary users of the computing systems.

For some examples, workloads may be containerized to allow executionacross many disparate computing systems. Containerization may provideseparation and security of the primary users' data.

FIG. 1A illustrates example instructions stored on a non-transitorycomputer-readable storage medium 100 to implement artificialintelligence-based distributed workload execution according to thepresent disclosure, and FIG. 1B illustrates an example computing device150 according to the present disclosure.

As shown in FIG. 1A, the non-transitory computer-readable storage medium100 includes instruction 102 that may cause a processor 110 (FIG. 1B) togenerate a neural network model 120 (FIG. 1B) to propagate onto acomputing network 300 (FIG. 3 ) having a plurality of computing systems305 a-305 d (FIG. 3 ). The neural network model 120 may make predictionsof computer resource utilization to generate a neural path for theexecution of workload across multiple computer resources. Thesepredictions may have greater accuracy as more data is analyzed.

Instruction 102 may include instruction 104, instruction 106 andinstruction 108. Instruction 104 may cause a processor to perform theoperation of learning a current utilization pattern for each one of thecomputing resources based on performance metrics and historical usagepatterns. Such performance metrics may include CPU utilization, RAMutilization, disk I/O activity and network performance (i.e., bandwidthor data transfer rate over the network).

Instruction 106 may cause processor 110 to perform the operation ofdetermining a workload capacity for each one of the computing systems305 a-305 d. (FIG. 3 ). The performance metrics and historical usagepatterns provide an initial estimate of the workload capacity for eachone of the computing systems 305 a-305 d. System utilization patternsmay be monitored, and real-time updates may allow the neural networkmodel 120 (FIG. 1B) to learn system utilization and refine workloadcapacity estimates for each one of the computing resources of thenetwork.

Instruction 108 may cause the processor 110 to perform the operation ofgenerating a neural path for execution of a specified workload. Theworkload execution cost for a specified workload may be determined. Theworkload execution cost is an estimation of the computing systemresources that may be used to execute the workload. The workloadexecution cost may include the time for executing the workload. Toobtain an accurate estimate of the workload execution cost of aspecified workload, the neural network model 120 may use the performancemetrics of the computing systems and workload execution time. The neuralnetwork model 120 may use the performance metrics and workload executiontime together to determine which systems are capable of executing theworkload and the amount of time for such systems to execute theworkload. The workload execution cost for a specified workload may beestimated by simulating a workload. Furthermore, the workload executioncost for a specified workload may be estimated by executing the actualworkload and analyzing system performance metrics and workload executiontime.

The non-transitory computer-readable storage medium 100 may be anyelectronic, magnetic, optical, or other physical storage device thatstores executable instructions. For example, the non-transitorycomputer-readable storage medium 100 may be a random access memory(RAM), an electrically-erasable programmable read-only memory (EEPROM),a storage drive, an optical disc, or the like. The non-transitorycomputer-readable storage medium 100 can be encoded to store executableinstructions that cause the processor 110 to perform operationsaccording to examples of the disclosure.

FIG. 2 illustrates a process according to some examples of the presentdisclosure. Process 200, illustrated in FIG. 2 may be representative ofcomputer readable instructions that may be executed by a processor toimplement a process for workload execution.

Process 200 begins with operation 202 at which the real-time performanceof each computing system of a network of computing systems may beanalyzed to determine a workload capacity for each computing system.Analyzing the real-time performance of each computing system may includeanalyzing CPU utilization, RAM utilization, disk I/O speed, instructionexecution speed, network bandwidth and floating-point operation speed.Analyzing the real-time performance of each computing system may includeanalyzing the availability of the computing system.

At operation 204, a workload execution cost to execute a workloadcontainer may be determined. As described above in reference to FIG. 1 ,a workload execution cost for a specified workload may be determined bymeasuring the performance metrics and execution time. For some examplesthe workload may be containerized to allow execution of distributedworkload across multiple resources. Containerization of the workload mayprovide separation and security of the primary users' data.Containerization is an alternative to full machine virtualization thatinvolves encapsulating a workload in a container with its own operatingenvironment. Containers may be designed to run on various platforms.Containerization of the workload may provide similar benefits toexecuting the workload on a virtual machine. This may address issues ofdata structure changes to meet data store demands. Workloadcontainerization may also allow data locking to protect the primaryusers' data from unauthorized access.

At operation 206, the workload capacity for each computing system andthe workload execution cost may be reported to each of the computingsystems of the network. The workload capacity for each computing systemand the workload execution cost information may be used to scheduleworkload executions. For example, based on the workload capacity foreach computing system and the workload execution cost, workloads may berequested, rejected or transferred. For some examples of the presentdisclosure, the workload capacity for each computing system and theworkload execution cost information may be broadcasted to each of theother computing systems of the network. For some examples of the presentdisclosure, the workload capacity for each computing system and theworkload execution cost information may be reported to a centralizedworkload management service for workload execution scheduling.

At operation 208, a neural path through the network may be generated.The neural path may meet the determined workload execution cost toexecute the workload container. A neural path may be generated based onavailable information that allows execution of a workload acrossmultiple computing systems.

At operation 210, the neural path may be dynamically updated based uponchanges to the workload capacity of one or more of the computingsystems. Initially, there may be some rejection of workload, but overtime the neural network model learns how the computing resources of thenetwork interact and may generate more accurate neural paths forworkload execution.

The neural path may be revised upon iterations of the process andupdated information. For example, a workload capacity of computingdevice may change over time and the generated neural path may no longermeet the determined workload execution cost or may not be as efficientas another neural path. As the neural network learns from iterations ofthe process, the generated neural path may be dynamically updated tomeet the determined workload execution cost.

FIG. 3 illustrates an example network of computing systems to implementartificial intelligence-based distributed workload execution accordingto some examples of the present disclosure. The computing network 300,shown in FIG. 3 , includes computing systems 305 a-305 d. The computingsystems 305 a-305 d are communicatively coupled to each other by way ofa network 310 (e.g., the Internet, an intranet, etc.). According toexamples of the present disclosure, a network may have any number ofcomputing systems and the computing systems may be communicativelycoupled to each other in various ways. According to examples of thedisclosure, computing systems may include or may be, for example, apersonal computer, a desktop computer, a mobile computer, a laptopcomputer, a notebook computer, a terminal, a workstation, a servercomputer, a network device, or any other suitable computing device.

As shown in FIG. 3 , each of the computing systems 305 a-305 d mayinclude computing component 320. Computing component 320 may include acontroller/CPU 325 that may be, for example, a CPU, a chip or anysuitable computing or computational device. Computing component 320 mayinclude an operating system 330, a memory 340, executable code 345, anda storage system 350 that may include input/output devices 355.

Controller/CPU 325 may be configured to carry out methods describedherein and/or to execute various modules. Each of the computing systems305 a-305 d may include more than one computing component 320, and oneor more computing components 320 may act as the components of acomputing system according to examples of the present disclosure.

Operating system 330 may be, or may include, any code designed and/orconfigured to perform tasks involving controlling or otherwise managingoperation of computing component 320. This may include schedulingexecution of software programs or enabling software programs or othermodules or units to communicate. As an example, operating system 330 maybe a commercial operating system. For some examples of the disclosure,the computing component 320 may include a computing device that does notuse an operating system (e.g., a microcontroller, ASIC, FPGA, or SOC).

Memory 340 may be implemented in various forms including random accessmemory (RAM), read-only memory (ROM), volatile or non-volatile memory, acache memory, or other suitable memory units or storage units. Memory340 may be a computer-readable non-transitory storage medium.

Executable code 345 may be any executable code, e.g., an application, aprogram, or a process. Executable code 345 may be executed bycontroller/CPU 325 possibly under control of operating system 330.Examples of the present disclosure may include a plurality of executablecode that may be loaded into memory 340 and cause controller/CPU 325 tocarry out methods described herein.

Storage system 350 may be or may include, for example, a hard diskdrive, flash memory, a micro controller-embedded memory, or removablestorage. Content may be stored in storage system 350 and may be loadedfrom storage system 350 into memory 340 where it may be processed bycontroller/CPU 325. Although shown as a separate component, storagesystem 350 may be embedded or included in memory 340.

Input/output devices 355 may include any suitable input devices such asa keyboard/keypad, mouse and any suitable output devices such asdisplays or monitors. A universal serial bus (USB) device or externalhard drive may be included in input/output devices 355. Any applicableinput/output devices may be connected to computing component 320 by, forexample, a wired or wireless network interface.

As discussed above, examples of the present disclosure may include acomputer-readable medium, which when executed by a processor may causethe processor to perform operations disclosed herein. According toexamples of the present disclosure, executable code 345 includesexecutable code implementing an AI workload capacity module 346 and aworkload execution management module 347 that may provide a neuralnetwork model for executing workload across computer systems.

According to examples of the disclosure, the workload capacity module346 on each computing system may continuously monitor systemutilization. For example, the workload capacity module 346 may analyzeCPU utilization, RAM utilization, disk I/O speed, instruction executionspeed, and floating-point operation speed of the computing system. Theworkload capacity module 346 may create a system utilization report andsend the system utilization report to the other computing systems 305a-305 d of the network 300 (FIG. 3 ). The system utilization report maybe synchronized for every computing system so that inconsistencies maybe corrected. Therefore, at any time, all of the computing systems knowthe availability of computing resources for all computing systems of thenetwork.

According to an example of the disclosure, the workload executionmanagement module 347 on each computing system determines a workloadexecution cost for each workload. The workload execution cost for aspecified workload may be determined by simulating a workload. Thesimulation may be based on the available computing resources of acomputing system to determine the workload execution time for eachcomputing system. The workload execution cost for a specified workloadmay also be determined by executing the workload and analyzing systemperformance metrics and workload execution time.

Using the computing resources availability information, the workloadexecution management module 347 may predict which computing system (orset of computing systems) has the available computing resources toprovide workload capacity to execute the workload. The workloadexecution management module 347 may then generate a neural path throughthe computing systems that meets the predicted execution cost of theworkload.

According to examples of the disclosure, the neural path may bedynamically updated based on changes to computing resources availabilitythroughout the network. For example, if a computing system that has beenassigned a workload becomes unavailable, the computing system may rejectthe workload and may report unavailability to all of the other computingsystems of the network. Other computing systems that have availableresources that meet the workload execution cost may accept the workload.

For some examples of the present disclosure the workload executionmanagement module 347 may generate multiple neural paths that meetsystems that meet the predicted execution cost of the workload. Asdiscussed above, the neural paths may be determined based on real-timeperformance measurements and may be disrupted, for example if a deviceis unexpectedly unavailable. In some examples, one or more contingencyneural paths may be generated in case of an interruption in executioncapacity of the neural path. This may allow a contingency neural path tobe used to execute a workload upon a disruption of primary neural path.

As discussed above, for some examples of the present disclosure, theworkload capacity for each computing system and the workload executioncost information as well as other information to implement the neuralnetwork workload execution model may be reported to each of the othercomputing systems of the network.

For such examples, a host computing system may review an opentransaction record and select a workload for execution based upon aworkload capacity of the computing system. If no other computing systemof the network is executing the workload, the host computing system maynotify the other computing systems of the network that the workload isbeing executed. Upon completion of execution, the host computing maynotify the other computing systems of the network. If workloadcompletion fails (e.g., the workload execution is interrupted), the hostcomputing system may suspend the workload execution and queue theworkload execution internally for execution at a later time or requestthat the workload execution be transferred to another computing systemof the network.

For some examples of the present disclosure, the workload capacity foreach computing system 305 a-305 d and the workload execution costinformation may be reported to a centralized workload management servicefor workload execution scheduling.

Workload execution requests may be sent to a centralized workloadmanagement service. The centralized workload management service maymanage all the workload execution requests. Workload execution requestsmay be queued and then sent to a host computing system 305 a having aworkload capacity that meets the workload execution cost of the workloadexecution request.

When the host computing system 305 a has received a workload executionrequest, the host computing system 305 a may send an acknowledgement tothe centralized workload management service and begin execution of theworkload. Upon completion of execution of the workload, the hostcomputing system 305 a may report completion to the centralized workloadmanagement service. If workload completion fails (e.g., the workloadexecution is interrupted), the host computing system 305 a may notifythe centralized workload management service that the workload cannot beexecuted at that time by the host computing system 305 a. Thecentralized workload management service may then transfer the workloadto a different computing system 305 b-d of the network 300 according toexamples of the present disclosure. If no computing systems areavailable to execute the workload, the centralized workload managementservice may queue the workload request until host computing resourcesare available to provide workload capacity that meets the workloadexecution cost of the workload execution request.

FIG. 4 illustrates a nodal network of computing resources 400 accordingto an example of the present disclosure. Nodal network 400 as shown inFIG. 4 includes nodes N₁-N₅. The term ‘node’ is used herein to refer toa computer system used for processing and routing transactions within anetwork of nodes. Nodes may include, for example, an individualcomputer, a server in an organization, or a data center operated by anorganization.

In general, a nodal network of computing resources according to examplesof the present disclosure may have any number of nodes, N₁-N_(n), andthe number of nodes may change at any time as nodes may be added orremoved to reflect computing resources added to or removed from thenetwork.

The neural network model may calculate workload capacity of thecomputing resources of the network in MIPS (millions of instructions persecond). The neural network model may calculate workload capacity of thecomputing resources of the network at various times as shown for examplein FIG. 4 as times t₁ and t₂. The values for workload capacity of FIG. 4may represent a pattern of resource availability learned by a neuralnetwork model based on iterations over a period of time.

As shown in FIG. 4 at time t₁ which may be, for example, during aparticular time period of a day or a particular day of the week, node N₁has a capacity of 25 MIPS. Node N₂ has a capacity of 20 MIPS, node N₃has a capacity of 30 MIPS, node N₄ has a capacity of 5 MIPS, and node N₅has a capacity of 20 MIPS. At time, t₁, the network may be able toexecute a workload total of 100 MIPS.

At time t₁, for example, for a workload having a total workloadexecution cost of 30 MIPS, the neural path may include node N₃ as theworkload can be executed by node N₃ without sharing. If node N₃ is notavailable as predicted or subsequently is disrupted, the neural path maybe dynamically updated to include node N₂ and node N₅, or the neuralpath may include other nodes having a combined total workload capacityof 30 MIPS or greater.

At time t₁, for example, for a workload having a total workloadexecution cost of 75 MIPS, the neural path may include node N₁, node N₃,and node N₄. If node N₁ is not available as predicted or subsequently isdisrupted, the neural path may be dynamically updated to include nodeN₂, node N₃, node N₄, and node N₅ to meet the total workload executioncost of 75 MIPS.

As shown in FIG. 4 at time, t₂ which may be, for example, during adifferent time period of a day or a different day of the week than t₁,node N₁ has a capacity of 5 MIPS, node N₂ has a capacity of 10 MIPS,node N₃ has a capacity of 0 MIPS, node N₄ has a capacity of 30 MIPS, andnode N₅ has a capacity of 5 MIPS. Therefore, at time t₂, the network maybe able to execute a workload total of 50 MIPS.

As illustrated, nodes may have different workload capacities atdifferent times as, for example, different time periods of a day ordifferent days of the week. So, for example, at time t₂, node N₃ has acapacity of 0 which may indicate that the computing system has beenremoved from the network or that a host user is using the entireworkload capacity of the computing system at time t₂. Neural pathsgenerated at time t₂ may not include node N₃.

Examples of the present disclosure may provide increased accuracy ofworkload execution predictions, and containerization of the workloadsmay provide separation and security of the primary user's data. Examplesof the present disclosure may allow efficient scheduling and use ofresources without adversely impacting the primary user.

Examples of the present disclosure may include a method to executeworkload using computing systems of a network of computing systems. Forexample, a real-time performance of each computing system may beanalyzed to determine a workload capacity of each computing system. Aworkload execution cost to execute a workload container may bedetermined. The workload capacity for each computing system and theworkload execution cost may be reported to each of the computingsystems. A neural path through the network that meets the determinedworkload execution cost to execute the workload container may then begenerated. The neural path may be dynamically updated based upon changesto the workload capacity of one or more of the computing systems.

Examples of the present disclosure may include a network of computingsystems. Each of the computing systems of the network may have aworkload capacity module and a workload execution management module. Theworkload capacity module may analyze a real-time performance of thecomputing system. The workload execution management module may determinea workload execution cost to execute a workload container and togenerate a neural path through the network. The neural path may meet thepredetermined workload execution cost to execute the workload container.

The various component computing systems as shown in FIG. 3 and FIG. 4are illustrative and are not intended to limit the scope of the presentinvention. The computing systems and/or nodes may instead each includemultiple interacting computing systems or devices, and the computingsystems/nodes may be connected to other devices including through one ormore networks such as the Internet, via the Web, or via privatenetworks. More generally, a computing system or node may comprise anycombination of hardware that may interact with and perform the describedtypes of functionality, optionally when programmed or otherwiseconfigured with particular software instructions and/or data structuresincluding without limitation desktop or other computers, databaseservers, network storage devices and other network devices. In addition,the functionality provided by the workload execution system as shown mayin some examples be distributed in various modules. Similarly, in someexamples, some of the functionality of the workload execution system maynot be provided and/or other additional functionality may be provided.

Furthermore, in some examples, some or all of the systems and/or modulesmay be implemented or provided in other manners such as by thoseconsisting of one or more means that are implemented at least partiallyin firmware and/or hardware rather than as a means implemented in wholeor in part by software instructions that configure a particularprocessor. The systems, modules, and data structures may also in someexamples be transmitted via generated data signals on a variety ofcomputer-readable transmission mediums, including wireless-based mediumsand may take a variety of forms. Accordingly, examples of the presentdisclosure may be practiced with other computer system configurations.

While the above description is a complete description of specificexamples of the disclosure, additional examples are also possible. Thus,the above description should not be taken as limiting the scope of thedisclosure which is defined by the appended claims along with their fullscope of equivalents.

1. A non-transitory, computer-readable storage medium having storedthereon instructions which when executed by a processor, cause theprocessor to perform operations comprising: generating a neural networkmodel to propagate onto a computing network having a plurality ofcomputing resources wherein said generating is by: learning a currentutilization pattern for each one of the computing resources based onperformance metrics and historical usage patterns; determining aworkload capacity for each one of the computing resources; andgenerating a neural path for execution of a specified workload.
 2. Thenon-transitory, computer-readable storage medium of claim 1, wherein theinstructions cause the processor to perform the further operationcomprising: dynamically updating the neural path based upon changes to aworkload capacity of a computing resource.
 3. The non-transitory,computer-readable storage medium of claim 1, wherein performance metricsinclude CPU utilization, RAM utilization, disk I/O speed, instructionexecution speed, network bandwidth and floating-point operation speed.4. The non-transitory, computer-readable storage medium of claim 1,wherein the specified workload is a containerized workload furthercomprising: determining a workload execution cost, the workloadexecution cost determined by analyzing performance metrics andcalculating an execution time for the containerized workload.
 5. Thenon-transitory, computer-readable storage medium of claim 4, whereincalculating an execution time includes assessing at least one of asimulated workload execution and an actual workload execution.
 6. Amethod of workload execution using computing systems of a network ofcomputing systems comprising: analyzing a real-time performance of eachcomputing system to determine a workload capacity of each computingsystem; determining a workload execution cost to execute a workloadcontainer; reporting the workload capacity for each computing system andthe workload execution cost to each of the computing systems; generatinga neural path through the network, the neural path meeting thedetermined workload execution cost to execute the workload container;and dynamically updating the neural path based upon changes to theworkload capacity of any of the computing systems of the neural path. 7.The method of claim 6, wherein analyzing a real-time performance of eachcomputing system includes analyzing CPU utilization, RAM utilization,disk I/O speed, instruction execution speed, and floating-pointoperation speed.
 8. The method of claim 6, wherein determining theworkload execution cost includes determining an execution time for theworkload container.
 9. The method of claim 8, wherein determining anexecution time includes assessing at least one of a simulated workloadexecution and an actual workload execution.
 10. The method of claim 6,further comprising: executing the workload container; and reportingexecution of the workload container to each computing system.
 11. Asystem comprising: a network of computing systems, each of the computingsystems having a workload capacity module to analyze a real-timeperformance of the computing system; a workload execution managementmodule to determine a workload execution cost to execute a workloadcontainer and generate a neural path through the network, the neuralpath meeting the determined workload execution cost to execute theworkload container.
 12. The system of claim 11, wherein the workloadcapacity module analyzes CPU utilization, RAM utilization, disk I/Ospeed, instruction execution speed, and floating-point operation speedof the computing system.
 13. The system of claim 11, wherein theworkload execution management module is a centralized workloadmanagement service to aggregate data from each of the plurality ofcomputing systems.
 14. The system of claim 13, wherein the workloadexecution management module generates at least one contingency neuralpath through the network.
 15. The system of claim 11, wherein theworkload execution management module calculates an execution time byperforming at least one of a simulated workload execution and an actualworkload execution.